How Much Does Distance to Train Stations Matter?

DATA1901 Project 2 report

1 Executive Summary

Based on data analysis using 2036 data points from five geographically close suburbs, we can conclude that distance from a train station has a small but relatively insignificant effect on townhouse prices. Therefore, new home buyers can choose more conveniently located homes without worrying about a significant increase in price.

2 Full Report

2.1 Initial Data Analysis (IDA)

2.1.1 Reading in files

We first generate a list of full list of names and the longitude and latitude of the train stations of these respective suburbs. The list is stored in main1_INPUT.txt (Appendix 1 ??). Then we have the R code (Appendix 2 ??) reading this .txt file to output the .csv files for each suburbs (Appendix 3 ?? Or github link). Finally we read in the data through the code below.

# Get the list of CSV files in the 'csv_cache' directory
csv_files <- list.files(path = "csv_cache", pattern = "*.csv", full.names = TRUE)

# Initialize an empty data frame to store the combined data
combined_df <- data.frame()

# Loop through each file in the csv_files list
for (file in csv_files) {
  # Read the CSV file
  location_data <- read.csv(file)
  
  # Categorize distance
  location_data$"distance_to_train_station(km)" <- location_data$distance_to_train_station / 1000
  
  # Classing distance
  location_data$distance_class <- cut(location_data$"distance_to_train_station(km)",
                                      breaks = c(0, 0.250, 0.500, 0.750, 1.000, 1.250, 1.500, 1.750, 2.000, 2.250, 2.500, 3.000, 3.250, 3.500, 3.750, 4.000))
  
  # Combine the processed data frame with the combined_df data frame
  combined_df <- rbind(combined_df, location_data)
}

# Inspect the combined number of suburbs
print(paste0("Total number of suburbs: ", length(csv_files)))

## [1] "Total number of suburbs: 136"

# Inspect the combined data frame
tail(combined_df)

##       House_ID                                   address bedroom bathroom
## 29781      810     7/24 Methven Street Mount Druitt 2770       3        1
## 29782      811     9/41 Methven Street Mount Druitt 2770       3        1
## 29783      812       1/21 Hythe Street Mount Druitt 2770       3        1
## 29784      813     1/14 Meacher Street Mount Druitt 2770       3        1
## 29785      814      4/34 Durham Street Mount Druitt 2770       3        1
## 29786      815 49/334 Woodstock Avenue Mount Druitt 2770       3       NA
##       carspace soldprice   yearsold  latitude longitude
## 29781        1    154000 2001-09-01 -33.76247  150.8216
## 29782        1    156000 2001-09-01 -33.76186  150.8249
## 29783        1    400000 2001-08-01 -33.76308  150.8211
## 29784        1    189000 2001-08-01 -33.76042  150.8203
## 29785        1    178000 2001-07-01 -33.77166  150.8122
## 29786       NA    124000 2001-06-01 -33.75742  150.8206
##       distance_to_train_station distance_to_train_station(km) distance_class
## 29781                  802.9798                     0.8029798       (0.75,1]
## 29782                  967.3295                     0.9673295       (0.75,1]
## 29783                  729.2326                     0.7292326     (0.5,0.75]
## 29784                 1019.4336                     1.0194336       (1,1.25]
## 29785                  768.9916                     0.7689916       (0.75,1]
## 29786                 1354.9054                     1.3549054     (1.25,1.5]

Data used for the report was scraped from the internet using the following link: https://www.auhouseprices.com/sold/list/NSW/.

In total, we analysed 136 suburbs across Sydney, containing a total of 29786 data entries. Each data entry contains a complete buy/sell history.

We used these variables and cleaned the data in the following ways:

• Distance from train station (km) [QUANTITATIVE] Address was operationalised into longitude and latitude. These coordinates were used to calculate straight line distance to train station and classed into 250m intervals
• Selling price [QUANTITATIVE]

2.1.2 Limitations

A function was created to calculate straight line distance from townhouses to train stations, which inaccurately represents travel distance between the two. Some townhouses are likely closer to stations from neighbouring suburbs instead. The relevance of trains as a mode of transport may differ between different suburbs. Additionally, train stations often coincide with commercial centres which may affect selling price.

2.1.3 Assumptions

A significant assumption was that no amenities close to train stations would increase the price of townhouses (e.g. shops, schools), which may be confounding variables. Another assumption was that all stations, regardless of how major, had an equal effect on selling prices.

2.2 Research Question

What is the effect of distance to train stations on Sydney’s housing prices?

2.3 Research Theme

Distances from stations were classed into 250 metre intervals to increase the readability of graphical summaries, as the data points produced cluttered scatterplots. A side-by-side boxplot was used to compare whether distance correlated to a change in price. The boxplot suggests there is no correlation between proximity to train stations and selling price. The residual plot illustrates clustering of data points on the bottom-left. Without random scatter, the data is not homoscedastic, hence a linear model is not appropriate.

The numerical summary suggested no correlation. The median selling price for houses between 0 and 250 metres was $506000, it increased to $560000 between 1.75 and 2 kilometres, then decreased to $360000 between 3.75 and 4 kilometres. The fluctuation in median selling price over distance discounts the possibility of a linear correlation. Properties in Sydney within 400 metres of train stations have higher price growth (4.5%) compared to properties between 800 and 1600 metres (0.3%)(Forbes, 2021). Other research suggests the train stations have an insignificant correlation with property prices (r=0.091) (p=0.380)(Berawi et al., 2020). Research suggests that number of rooms and building size was the most significant contributor to property pricing close to stations(Berawi et al., 2020). From our graphs, we see that an increase in car spaces and bathrooms was also linked to an increase in price, and so this could potentially be a confounding variable.

The number of confounding variables alongside a more complex trend could account for the lack of correlation observed. Prices seemed to increase with the number of bedrooms, car-spaces and bathrooms. Yet after controlling for them, there was still no correlation. This suggests there are further confounding variables unaccounted for.To account for inflation, a boxplot of selling price between 2000 and 2023 in Western Sydney suburbs was plotted. There was a general increase in townhouse price over the years. Inflation is also a significant confounding variable that has had a substantial effect on selling price. The complex interaction of variables which affect property price could explain the absence of a correlation.

2.4 Related Articles

Proximity to schools and hospitals had a significant effect on house pricing(Berawi et al., 2020). Positive effects of stations on property pricing include retail activity, reduced travel cost or convenience whilst increased crime or noise have negative effects(Bowes & Ihlanfeldt, 2001). These confounding variables could explain the null result and must be controlled in further investigation.

2.5 References

Berawi, M. A., Miraj, P., Saroji, G., & Sari, M. (2020). Impact of rail transit station proximity to commercial property prices: Utilizing big data in Urban Real Estate. Journal of Big Data, 7(1), 1–17. https://doi.org/10.1186/s40537-020-00348-z

Bowes, D. R., & Ihlanfeldt, K. R. (2001). Identifying the impacts of rail transit stations on residential property values. Journal of Urban Economics, 50(1), 1–25. https://doi.org/10.1006/juec.2001.2214

Forbes, K. (2021, August 12). Does a train station increase the value of a property? Metropole Property Strategists. Retrieved April 10, 2023, from https://metropole.com.au/how-have-train-stations-affected-property-prices-in-sydney/#:~:text=It%20found%20that%20properties%20within,a%20growth%20rate%20of%200.3%25.

2.6 Acknowledgments

When did you team meet (date and time), and what did each team member contribute?

??

2.7 Appendix (Optional)

2.8 Filtering Data

combined_df_1bed <-filter(combined_df, bedroom ==1)
combined_df_2bed <-filter(combined_df, bedroom ==2)
combined_df_3bed <-filter(combined_df, bedroom ==3)
combined_df_4bed <-filter(combined_df, bedroom ==4)
combined_df_5bed <-filter(combined_df, bedroom ==5)

par(mfrow=c(1,2))
ggplot(combined_df_1bed, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs Distance from Train Station for 1 Bedroom", x="Distance from Train Station(km)", y="Selling Price (x$100000)", fill = "Number of Carspaces")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))+
  theme(plot.title = element_text(hjust=0.25))

ggplot(combined_df_1bed, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5, aes(fill=factor(carspace))) +
  labs(title = "Sold Price vs Distance from Train Station for 1 Bedroom", x="Distance from Train Station(km)", y="Selling Price (x$100000)", fill = "Number of Carspaces")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))+
  theme(plot.title = element_text(hjust=0.25))

ggplot(combined_df_2bed, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs Distance from Train Station for 2 Bedrooms", x="Distance from Train Station(km)", y="Selling Price (x$100000)", fill = "Number of Carspaces")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))+
  theme(plot.title = element_text(hjust=0.25))

summary(combined_df_2bed$soldprice)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 5.000e+04 3.500e+05 4.680e+05 7.890e+05 6.120e+05 2.147e+09

ggplot(combined_df_2bed, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5, aes(fill=factor(carspace))) +
  labs(title = "Sold Price vs Distance from Train Station for 2 Bedrooms", x="Distance from Train Station(km)", y="Selling Price (x$100000)", fill = "Number of Carspaces")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))+
  theme(plot.title = element_text(hjust=0.25))

summary(combined_df_2bed$soldprice)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 5.000e+04 3.500e+05 4.680e+05 7.890e+05 6.120e+05 2.147e+09

ggplot(combined_df_3bed, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs Distance from Train Station for 3 Bedrooms", x="Distance from Train Station(km)", y="Selling Price (x$100000)", fill = "Number of Carspaces")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))+
  theme(plot.title = element_text(hjust=0.25))

summary(combined_df_3bed$soldprice)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##      575   425000   570000   642811   745000 22867454

ggplot(combined_df_3bed, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5, aes(fill=factor(carspace))) +
  labs(title = "Sold Price vs Distance from Train Station for 3 Bedrooms", x="Distance from Train Station(km)", y="Selling Price (x$100000)", fill = "Number of Carspaces")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))+
  theme(plot.title = element_text(hjust=0.25))

summary(combined_df_3bed$soldprice)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##      575   425000   570000   642811   745000 22867454

ggplot(combined_df_4bed, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs Distance from Train Station for 4 Bedrooms", x="Distance from Train Station(km)", y="Selling Price (x$100000)", fill = "Number of Carspaces")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))+
  theme(plot.title = element_text(hjust=0.25))

summary(combined_df_4bed$soldprice)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     1650   534300   675000   763610   866000 15000000

ggplot(combined_df_4bed, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5, aes(fill=factor(carspace))) +
  labs(title = "Sold Price vs Distance from Train Station for 4 Bedrooms", x="Distance from Train Station(km)", y="Selling Price (x$100000)", fill = "Number of Carspaces")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))+
  theme(plot.title = element_text(hjust=0.25))

summary(combined_df_4bed$soldprice)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     1650   534300   675000   763610   866000 15000000

ggplot(combined_df_5bed, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs Distance from Train Station for 5 Bedrooms", x="Distance from Train Station(km)", y="Selling Price (x$100000)", fill = "Number of Carspaces")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))+
  theme(plot.title = element_text(hjust=0.25))

summary(combined_df_5bed$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  200000  659990  783000  864065  930000 3080000

ggplot(combined_df_5bed, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5, aes(fill=factor(carspace))) +
  labs(title = "Sold Price vs Distance from Train Station for 5 Bedrooms", x="Distance from Train Station(km)", y="Selling Price (x$100000)", fill = "Number of Carspaces")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))+
  theme(plot.title = element_text(hjust=0.25))

summary(combined_df_5bed$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  200000  659990  783000  864065  930000 3080000

3 Filtering Data by Carspaces and Bedrooms

combined_df_1bed_1car <-filter(combined_df, bedroom ==1, carspace == 1)

combined_df_2bed_1car <-filter(combined_df, bedroom ==2, carspace == 1)

combined_df_2bed_2car <-filter(combined_df, bedroom ==2, carspace == 2)

combined_df_3bed_1car <-filter(combined_df, bedroom ==3, carspace == 1)

combined_df_3bed_2car <-filter(combined_df, bedroom ==3, carspace == 2)

combined_df_3bed_3car <-filter(combined_df, bedroom ==3, carspace == 3)

combined_df_3bed_4car <-filter(combined_df, bedroom ==3, carspace == 4)

combined_df_4bed_1car <-filter(combined_df, bedroom ==4, carspace == 1)

combined_df_4bed_2car <-filter(combined_df, bedroom ==4, carspace == 2)

combined_df_4bed_3car <-filter(combined_df, bedroom ==4, carspace == 3)

combined_df_4bed_4car <-filter(combined_df, bedroom ==4, carspace == 4)

combined_df_5bed_1car <-filter(combined_df, bedroom ==5, carspace == 1)

combined_df_5bed_2car <-filter(combined_df, bedroom ==5, carspace == 2)

combined_df_5bed_3car <-filter(combined_df, bedroom ==5, carspace == 3)

3.0.0.1 1 bedroom

ggplot(combined_df_1bed_1car, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs Distance from Train Station for 1 Bedroom and 1 Carspace", x="Distance from Train Station(km)", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_1bed_1car$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   61325  253000  375000  402900  487225 1330000

3.0.0.2 2 bedrooms

ggplot(combined_df_2bed_1car, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs Distance from Train Station for 2 Bedrooms and 1 Carspace", x="Distance from Train Station(km)", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_2bed_1car$soldprice)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 5.700e+04 3.400e+05 4.600e+05 8.584e+05 6.000e+05 2.147e+09

ggplot(combined_df_2bed_2car, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs Distance from Train Station for 2 Bedrooms and 2 Carspaces", x="Distance from Train Station(km)", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_2bed_2car$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   66000  432625  547500  588482  700000 1581000

3.0.0.3 3 bedrooms

ggplot(combined_df_3bed_1car, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs Distance from Train Station for 3 Bedrooms and 1 Carspace", x="Distance from Train Station(km)", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_3bed_1car$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   50000  360000  500000  549951  655000 5850000

ggplot(combined_df_3bed_2car, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs Distance from Train Station for 3 Bedrooms and 2 Carspaces", x="Distance from Train Station(km)", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_3bed_2car$soldprice)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##      575   500000   637000   723621   840000 22867454

ggplot(combined_df_3bed_3car, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs Distance from Train Station for 3 Bedrooms and 3 Carspaces", x="Distance from Train Station(km)", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_3bed_2car$soldprice)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##      575   500000   637000   723621   840000 22867454

ggplot(combined_df_3bed_4car, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs Distance from Train Station for 3 Bedrooms and 4 Carspaces", x="Distance from Train Station(km)", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_3bed_4car$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  305000  478500  590000  668278  809000 1600000

3.0.0.4 4 bedrooms

ggplot(combined_df_4bed_1car, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs Distance from Train Station for 4 Bedrooms and 1 Carspace", x="Distance from Train Station(km)", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_4bed_1car$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  175000  450000  620000  661696  770000 2750000

ggplot(combined_df_4bed_2car, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs Distance from Train Station for 4 Bedrooms and 2 Carspaces", x="Distance from Train Station(km)", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_4bed_2car$soldprice)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     1650   555000   690000   790771   890000 15000000

ggplot(combined_df_4bed_3car, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs Distance from Train Station for 4 Bedrooms and 3 Carspaces", x="Distance from Train Station(km)", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_4bed_3car$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  100000  615250  769000  927646 1023250 3000000

ggplot(combined_df_4bed_4car, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs Distance from Train Station for 4 Bedrooms and 4 Carspaces", x="Distance from Train Station(km)", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_4bed_4car$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  330000  535500  655000  715113  840000 1630000

3.0.0.5 5 bedrooms

ggplot(combined_df_5bed_1car, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs Distance from Train Station for 5 Bedrooms and 1 Carspace", x="Distance from Train Station(km)", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_5bed_1car$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  335000  602500  722500  743607  870000 1850000

ggplot(combined_df_5bed_2car, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs Distance from Train Station for 5 Bedrooms and 2 Carspaces", x="Distance from Train Station(km)", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_5bed_2car$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  200000  663748  820000  900051  960416 3080000

ggplot(combined_df_5bed_3car, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs Distance from Train Station for 5 Bedrooms and 3 Carspaces", x="Distance from Train Station(km)", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_5bed_3car$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  700000  738000  790000  787833  838250  910000

4 Creating a column for Year

combined_df$Year <- as.factor(format(as.Date(combined_df$yearsold), "%Y"))

# Filtering by year
combined_df_0.00 <-filter(combined_df, distance_class == "(0,0.25]")
combined_df_0.25 <-filter(combined_df, distance_class == "(0.25,0.5]")
combined_df_0.50 <-filter(combined_df, distance_class == "(0.5,0.75]")
combined_df_0.75 <-filter(combined_df, distance_class == "(0.75,1]")
combined_df_1.00 <-filter(combined_df, distance_class == "(1,1.25]")
combined_df_1.25 <-filter(combined_df, distance_class == "(1.25,1.5]")
combined_df_1.50 <-filter(combined_df, distance_class == "(1.5,1.75]")
combined_df_1.75 <-filter(combined_df, distance_class == "(1.75,2]")
combined_df_2.00 <-filter(combined_df, distance_class == "(2,2.25]")
combined_df_2.25 <-filter(combined_df, distance_class == "(2.25,2.5]")
combined_df_2.50 <-filter(combined_df, distance_class == "(2.5,2.75]")
combined_df_2.75 <-filter(combined_df, distance_class == "(2.75,3]")
combined_df_3.00 <-filter(combined_df, distance_class == "(3,3.25]")
combined_df_3.25 <-filter(combined_df, distance_class == "(3.25,3.5]")
combined_df_3.50 <-filter(combined_df, distance_class == "(3.5,3.75]")
combined_df_3.75 <-filter(combined_df, distance_class == "(3.75,4]")

ggplot(combined_df_0.00, aes(x = Year, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs year for townhouses 0 to 0.25km from train station", x="Year", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_0.00$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   50000  415000  565000  655697  780000 3300000

ggplot(combined_df_0.25, aes(x = Year, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs year for townhouses 0.25 to 0.50km from train station", x="Year", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_0.25$soldprice)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 5.000e+04 4.500e+05 5.975e+05 1.106e+06 7.910e+05 2.147e+09

ggplot(combined_df_0.50, aes(x = Year, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs year for townhouses 0.50 to 0.75km from train station", x="Year", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_0.50$soldprice)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     1650   425000   582500   652451   775000 15000000

ggplot(combined_df_0.75, aes(x = Year, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs year for townhouses 0.75 to 1.00km from train station", x="Year", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_0.75$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   60000  420000  549475  612502  718375 6203000

ggplot(combined_df_1.00, aes(x = Year, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs year for townhouses 1.00 to 1.25km from train station", x="Year", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_1.00$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     575  392500  540000  606991  720000 4400000

ggplot(combined_df_1.25, aes(x = Year, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs year for townhouses 1.25 to 1.50km from train station", x="Year", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_1.25$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   61325  378000  524000  572350  680000 4840000

ggplot(combined_df_1.50, aes(x = Year, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs year for townhouses 1.50 to 1.75km from train station", x="Year", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_1.50$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   92000  374462  522250  563676  650000 2812000

ggplot(combined_df_1.75, aes(x = Year, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs year for townhouses 1.75 to 2.00km from train station", x="Year", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_1.75$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  100000  359250  509500  566946  680000 3100000

ggplot(combined_df_2.00, aes(x = Year, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs year for townhouses 2.00 to 2.25km from train station", x="Year", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_2.00$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  132500  370000  490275  529213  620000 2430000

ggplot(combined_df_2.25, aes(x = Year, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs year for townhouses 2.25 to 2.50km from train station", x="Year", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_2.25$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  190000  382500  550000  592289  687000 5346000

ggplot(combined_df_2.50, aes(x = Year, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs year for townhouses 2.50 to 2.75km from train station", x="Year", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_2.50$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 

ggplot(combined_df_2.75, aes(x = Year, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs year for townhouses 2.75 to 3.00km from train station", x="Year", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_2.75$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 

ggplot(combined_df_3.00, aes(x = Year, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs year for townhouses 3.00 to 3.25km from train station", x="Year", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_3.00$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  180000  357500  500101  538112  600000 1777000

ggplot(combined_df_3.25, aes(x = Year, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs year for townhouses 3.25 to 3.75km from train station", x="Year", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_3.25$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  225000  300000  460000  446274  543000 1125000

ggplot(combined_df_3.50, aes(x = Year, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs year for townhouses 3.50 to 3.75km from train station", x="Year", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_3.50$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  250000  300000  355000  386685  441000  664000

ggplot(combined_df_3.75, aes(x = Year, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs year for townhouses 3.75 to 4.00km from train station", x="Year", y="Selling Price (x$100000)")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))

summary(combined_df_3.75$soldprice)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 

ggplot(combined_df, aes(x = Year, y = soldprice/100000))+
    geom_point(aes(color=distance_class)) +
    labs(title = "Sold Price over Years", x="Year", y="Selling Price (x$100000)", fill = "Number of Carspaces")+
    theme_bw()+
    theme(axis.text.x = element_text(angle=45,hjust=1))+
    theme(plot.title = element_text(hjust=0.25))

ggplot(combined_df, aes(x = Year, y = soldprice/100000))+
    geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
    labs(title = "Sold Price over Years", x="Year", y="Selling Price (x$100000)", fill = "Number of Carspaces")+
    theme_bw()+
    theme(axis.text.x = element_text(angle=45,hjust=1))+
    theme(plot.title = element_text(hjust=0.25))

ggplot(combined_df, aes(x = factor(bedroom), y = soldprice/100000))+
    geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
    labs(title = "Sold Price for Different Numbers of Bedrooms", x="Number of Bedrooms", y="Selling Price (x$100000)")+
    theme_bw()+
    theme(axis.text.x = element_text(angle=45,hjust=1))+
    theme(plot.title = element_text(hjust=0.25))

ggplot(combined_df, aes(x = factor(bathroom), y = soldprice/100000))+
    geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
    labs(title = "Sold Price for Different Numbers of Bathrooms", x="Number of Bathrooms", y="Selling Price (x$100000)")+
    theme_bw()+
    theme(axis.text.x = element_text(angle=45,hjust=1))+
    theme(plot.title = element_text(hjust=0.25))

ggplot(combined_df, aes(x = factor(carspace), y = soldprice/100000))+
    geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
    labs(title = "Sold Price for Different Numbers of Carspaces", x="Number of Carspaces", y="Selling Price (x$100000)")+
    theme_bw()+
    theme(axis.text.x = element_text(angle=45,hjust=1))+
    theme(plot.title = element_text(hjust=0.25))

5 Two added graphs from Jasmine Mon Apr 17, 2023 7 pm

q1 <- quantile(combined_df$soldprice, 0.25)
q3 <- quantile(combined_df$soldprice, 0.75)
iqr <- q3 - q1
combined <- subset(combined_df, soldprice >= q1 - 1.5*iqr & soldprice <= q3 + 1.5*iqr)

# I changed the `na.rm` to be TRUE to remove all invalid N/A data points
Q1 <- quantile(combined_df$`distance_to_train_station(km)`, 0.25, na.rm = TRUE)
Q3 <- quantile(combined_df$`distance_to_train_station(km)`, 0.75, na.rm = TRUE)
IQR <- Q3 - Q1

# What I've changed here at 7:05 AM, Apr 17, 2023, Monday
# `subset(combined_df ...` <- `subset(combined, ...`
combined <- subset(combined_df, `distance_to_train_station(km)` >= Q1 - 1.5*IQR & `distance_to_train_station(km)` <= Q3 + 1.5*IQR)

ggplot(combined, aes(x = distance_class, y = soldprice/100000))+
geom_boxplot(outlier.colour = "blue", outlier.size=1.5) +
  labs(title = "Sold Price vs Distance from Train Station", x="Distance from Train Station(km)", y="Selling Price (x$100000)", fill = "Number of Carspaces")+
  theme_bw()+
  theme(axis.text.x = element_text(angle=45,hjust=1))+
  theme(plot.title = element_text(hjust=0.25))

model <- lm(soldprice ~ `distance_to_train_station(km)`, data = combined)
plot(combined$"distance_to_train_station(km)", resid(model), main = "Residual Plot", xlab = "Distance to train station (km)", ylab = "Residuals", cex=0.15)
abline(h=0)